System for identifying information represented by biological signals

ABSTRACT

This system for identifying information represented by biological signals is configured so as to detect biological signals (S501), analyze the detected biological signals and then output feature data (S502), determine the respective similarities between the feature data and a plurality of teaching data (S503), store the similarities per time in a time series (S504), and determine information represented by the biological signals on the basis of the plurality of similarities within a prescribed period among the stored similarities in the time series (S505).

TECHNICAL FIELD

The present invention relates to a system for identifying informationrepresented by a biological signal.

BACKGROUND ART

Efforts to use biological signals such as myoelectric signals to controlequipment such as wheelchairs, prosthetic hands, and prosthetic feethave been ongoing (Patent Literature 1).

CITATION LIST Patent Literature

[PTL 1] Japanese Laid-Open Publication No. 2001-331250

SUMMARY OF INVENTION Technical Problem

However, precision to identify the motion represented by a biologicalsignal such as a myoelectric signal is still not adequate. The precisionof identification is low, especially when the level of biological signalis low or when biological signals of a plurality of motions coexist.

The objective of the present invention is to provide a system foridentifying information represented by a biological signal, whichenables enhanced precision to identify a biological signal to solve theaforementioned problem. Another objective is to provide a fingerrehabilitation apparatus and a swallow diagnosis apparatus that applythe system for identifying information represented by a biologicalsignal in the field of rehabilitation or diagnosis.

Solution to Problem

The present invention provides, for example, the following items.

(Item 1)

A system for identifying information represented by a biological signal,the system comprising:

detection means for detecting a biological signal;

analysis means for analyzing the detected biological signal andoutputting feature data;

first determination means for determining degrees of similarity betweenthe feature data and each of a plurality of teaching data;

storage means for chronologically storing the degrees of similarity foreach time; and

second determination means for determining information represented bythe biological signal based on a plurality of degrees of similaritywithin a predetermined period in the chronological degrees of similaritystored in the storage means.

(Item 2)

The system of item 1, wherein the second determination means:

calculates computed values for each of the plurality of teaching databased on the plurality of degrees of similarity within the predeterminedperiod; and

extracts teaching data corresponding to the highest computed value amongthe computed values and determines information indicated by theextracted teaching data as the information represented by the biologicalsignal.

(Item 3)

The system of item 1, wherein the second determination means:

calculates computed values for each of the plurality of teaching databased on the plurality of degrees of similarity within the predeterminedperiod; and

extracts at least one teaching data corresponding to a computed valueexceeding a predetermined threshold value among the computed values anddetermines information indicated by the extracted teaching data as theinformation represented by the biological signal.

(Item 4)

The system of item 3, wherein the second determination means extracts aplurality of teaching data corresponding to computed values exceedingthe predetermined threshold value among the computed values anddetermines information indicated by each of the plurality of extractedteaching data as the information represented by the biological signal.

(Item 5)

The system of item 4, wherein the information represented by thebiological signal indicates that a composite motion has been performed.

(Item 6)

The system of any one of items 2 to 5, wherein the computed values aretotal values.

(Item 7)

The system of any one of items 1 to 6, wherein the storage means is abuffer for temporarily storing information, and the degrees ofsimilarity are temporarily stored in the buffer.

(Item 8)

The system of any one of items 1 to 7, wherein the predetermined periodis about 80 to 200 ms.

(Item 9)

The system of any one of items 1 to 8, further comprising wearing meansfor wearing the detection means on a body of a subject.

(Item 10)

The system of item 9, wherein the body is an upper limb, an abdomen, aneck, a lower limb, or a back of the subject.

(Item 11)

The system of any one of items 1 to 10 for finger rehabilitation, forswallow diagnosis, for a wheelchair, for a prosthetic hand, for aprosthetic arm, for a prosthetic foot, for a robot, for an upper limbassisting apparatus, for a lower limb assisting apparatus, or for atrunk assisting apparatus.

(Item 12)

A finger rehabilitation apparatus comprising:

the system of any one of items 1 to 11; and

a finger movement assisting apparatus.

(Item 13)

A swallow diagnosis apparatus comprising the system of any one of items1 to 11.

Advantageous Effects of Invention

The present invention can provide a system for identifying informationrepresented by a biological signal, which enables enhanced precision toidentify a biological signal. The present invention can also provide afinger rehabilitation apparatus and a swallow diagnosis apparatus thatapply the system for identifying information represented by a biologicalsignal in the field of rehabilitation or diagnosis.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram showing an example of a configuration of the system10 for identifying information represented by a biological signal of theinvention.

FIG. 2 is a diagram showing an example of a configuration of a computerapparatus 200.

FIG. 3 is a diagram showing an example of a configuration of a neuralnetwork 300 used by first determination means 222.

FIG. 4 is a diagram showing an example of a data configuration ofdegrees of similarity stored in a buffer of a memory unit 230.

FIG. 5 is a flow chart showing an example of the processing foridentifying information represented by a biological signal of theinvention.

FIG. 6A is a diagram showing an example of degrees of similarity storedin a buffer of a memory unit 230.

FIG. 6B is a diagram explaining that second determination means 223obtains total values by totaling a plurality of degrees of similaritywithin a predetermined period in chronological degrees of similaritystored in a buffer for each teaching data.

FIG. 6C is a diagram explaining that second determination means 223obtains total values by totaling a plurality of degrees of similaritywithin a predetermined period in chronological degrees of similaritystored in a buffer for each teaching data.

FIG. 6D is a diagram showing another example of degrees of similaritystored in a buffer of the memory unit 230.

FIG. 6E is a diagram explaining that second determination means 223obtains total values by totaling a plurality of degrees of similaritywithin a predetermined period in chronological degrees of similaritystored in a buffer for each teaching data.

FIG. 6F is a diagram explaining that second determination means 223obtains total values by totaling a plurality of degrees of similaritywithin a predetermined period in chronological degrees of similaritystored in a buffer for each teaching data.

FIG. 7A is a diagram showing the outer appearance of finger movementassisting apparatus 700.

FIG. 7B is a diagram showing the finger movement assisting apparatus 700worn on a finger of a user.

FIG. 8 is a graph showing results of an experiment for identifying amotion represented by a myoelectric signal detected from a myoelectricsensor that is worn on the skin of an upper limb of a subject.

FIG. 9 is a graph expanding the dotted line portion of the graph in FIG.8.

FIG. 10 is a graph showing results of a test for identifying a motionrepresented by a myoelectric signal detected from a myoelectric sensorthat is worn on the skin of an upper limb of a subject with a low levelof myoelectric signal.

FIG. 11 is a graph expanding the dotted line portion of the graph inFIG. 10.

DESCRIPTION OF EMBODIMENTS

The present invention is described hereinafter. The terms used hereinshould be understood as being used in the meaning that is commonly usedin the art, unless specifically noted otherwise. Therefore, unlessdefined otherwise, all terminologies and scientific technical terms thatare used herein have the same meaning as the general understanding ofthose skilled in the art to which the present invention pertains. Incase of a contradiction, the present specification (including thedefinitions) takes precedence.

1. Definitions of the Terms

As used herein, “biological signal” refers to a signal emitted by anorganism. Examples of biological signals include, but are not limitedto, myoelectric signals indicating the activity of a muscle of anorganism, cardioelectric signals indicating the activity of the heart ofan organism, brainwaves indicating the activity of a brain of anorganism, neural signals transmitted in neurons, and the like.Biological signals as used herein refers to a scalar quantity extractedfrom measured biological signals in a broad sense. Use of biologicalsignals in scalar quantity eliminates coordinate dependency, so thatvery high versatility and convenience can be attained. A specific valueof a biological signal can be associated with specific information(e.g., a specific motion of an organism (hand grasping motion, handopening motion, laughing motion, etc.) or a specific condition of anorganism (e.g., degree or type of muscle fatigue, etc.)) herein.

As used herein, “feature data” refers to multidimensional data obtainedby analyzing a biological signal in a scalar quantity.

As used herein, “teaching signal” refers to a signal for teaching that aspecific value of a biological signal represents specific information.For example, a teaching signal can teach that a specific value of abiological signal represents a specific motion of an organism. Forexample, a teaching signal can teach that a specific value of abiological signal represents a specific condition of an organism.

As used herein, “teaching data” refers to multidimensional datacorresponding to a teaching signal. The dimensionality of teaching datacorresponds to the number of pieces of information to be identified. If,for example, five pieces of information are taught, the dimensionalityof teaching data is at least five, and the teaching data is representedby (a, b, c, d, e) (0≤a, b, c, d, e≤1). For example, teaching data 1corresponding to teaching signal “1”, which teaches that the informationis the first information, can be (1.0, 0.0, 0.0, 0.0, 0.0), teachingdata 2 corresponding to teaching signal “2”, which teaches that theinformation is the second information, can be (0.0, 1.0, 0.0, 0.0, 0.0),teaching data 3 corresponding to teaching signal “3”, which teaches thatthe information is the third information, can be (0.0, 0.0, 1.0, 0.0,0.0), teaching data 4 corresponding to teaching signal “4”, whichteaches that the information is the fourth information, can be (0.0,0.0, 0.0, 1.0, 0.0), and teaching data 5 corresponding to teachingsignal “5”, which teaches that the information is the fifth information,can be (0.0, 0.0, 0.0, 0.0, 1.0).

As used herein, “about” means that the number described after said termis within the range of the number ±10%.

The embodiments of the invention are described hereinafter withreference to the drawings.

2. Configuration of the System for Identifying Information Representedby Biological Signal of the Invention

FIG. 1 shows an example of a configuration of the system 10 foridentifying information represented by a biological signal of theinvention. The system 10 comprises biological signal detection means 100and a computer apparatus 200. The biological signal detection means 100can be any means configured to detect a biological signal and output thedetected biological signal. For example, the biological signal detectionmeans 100 can be a myoelectric device comprising a myoelectric sensorcapable of detecting a myoelectric signal of an organism, anelectrocardiograph comprising a cardioelectric sensor capable ofdetecting a cardioelectric signal of an organism, a brainwave metercomprising a brainwave sensor capable of detecting a brainwave of anorganism, or the like. The biological signal detection means 100 can beconfigured to extract a scalar quantity from a detected biologicalsignal and output the scalar quantity if the detected biological signalis a biological signal with coordinate dependency (e.g., if the detectedbrainwave is a vector quantity (of coordinates of measurement location,intensity), if a myoelectric signal detected at an electrode on a filmis a vector quantity (of coordinates of measurement location,intensity), or the like). A database unit 250 is connected to thecomputer apparatus 200. The biological signal detection means 100 andthe computer apparatus 200 can be connected in any manner. For example,the biological signal detection means 100 and the computer apparatus 200can have a wired or wireless connection. For example, the biologicalsignal detection means 100 and the computer apparatus 200 can beconnected via a network (e.g., Internet, LAN, or the like). The computerapparatus 200 can be, for example, a computer apparatus that is usedtogether with the biological signal detection means 100 or a remoteserver apparatus located away from the biological signal detection means100.

The biological signal detection means 100 comprises a detection unit 110and a transmission unit 120.

The detection unit 110 can be any means configured to detect abiological signal. For example, the detection unit 110 can be amyoelectric sensor capable of detecting a myoelectric signal of anorganism, a cardioelectric sensor capable of detecting a myoelectricsignal of an organism, a brainwave sensor capable of detecting abrainwave of an organism, or the like. If, for example, the detectionunit 110 is a myoelectric sensor, the detection unit can comprise aprimary amplifier, a high pass filter, a low pass filter, a notchfilter, and a secondary amplifier for the detection of myoelectricsignals. Primary and secondary amplifiers are used for amplifying asignal. A high pass filter is used for attenuating a signal with afrequency lower than a predetermined frequency, such as a signal with afrequency lower than about 10 Hz. A low pass filter is used forattenuating a signal with a frequency higher than a predeterminedfrequency, such as a signal with a frequency higher than about 500 Hz. Anotch filter is used for attenuating a 50 to 60 Hz AC noise, which is atypical electrical noise. A band elimination filter can also be used inplace of a notch filter.

The transmission unit 120 is configured to be capable of transmitting asignal out of the biological signal detection means 100. Thetransmission unit 120 transmits a signal out of the biological signaldetection means 100 via a wireless or wired connection. For example, thetransmission unit 120 can transmit a signal by utilizing a wireless LANsuch as Wi-Fi. The transmission unit 120 can transmit a signal byutilizing a short range wireless communication or the like such asBluetooth®. For example, the transmission unit 120 transmits abiological signal detected by the detection unit 110 to the computerapparatus 200.

For example, teaching data corresponding to a teaching signal inputtedduring the learning stage can be stored in the database unit 250 whilebeing associated with inputted feature data. If, for example, a teachingsignal is inputted by a user during the usage stage, teaching datacorresponding to the inputted teaching signal can be stored in thedatabase unit 250 while being associated with the feature data at thetime.

FIG. 2 shows an example of a configuration of the computer apparatus200.

The computer apparatus 200 comprises a receiver unit 210, a processorunit 220, a memory unit 230, and an output unit 240.

The receiver unit 210 is configured to be capable of receiving a signalfrom the outside of the computer apparatus 200. The receiver unit 210receives a signal from the outside of the computer apparatus 200 via awireless or wired connection. The receiver unit 210 can receive a signalby utilizing a wireless LAN such as Wi-Fi. The receiver unit 210 canreceive a signal by utilizing a short range wireless communication orthe like such as Bluetooth®. The receiver unit 210 receives a biologicalsignal detected by the biological signal detection unit 100 from thebiological signal detection unit 100. For example, the receiver unit 210receives information stored in the database unit 250 from the databaseunit 250. The receiver unit 210 receives, for example, teaching signalsfor various information.

The processor unit 220 controls the operation of the entire computerapparatus 200. The processor unit 220 reads out a program stored in thememory unit 230 and executes the program. This allows the computerapparatus 200 to function as an apparatus that executes a desired step.

The memory unit 230 stores a program required to execute processing,data required to execute the program, and the like. For example, aprogram for materializing processing for identifying informationrepresented by a biological signal (e.g., processing discussed below inFIG. 5) can be stored in the memory unit 230. In this regard, theprogram can be stored in the memory unit 230 in any manner. For example,a program can be preinstalled in the memory unit 230. Alternatively, aprogram can be installed in the memory unit 230 by downloading theprogram through a network, or installed in the memory unit 230 via astorage medium such as an optical disk or USB.

The output unit 240 is configured to be capable of outputting a signalout of the computer apparatus 200. The output unit 240 can output asignal to anywhere. The output unit 240 can output a signal to anyhardware or software. The output unit 240 can output a signal in anymanner. For example, the output unit 240 can transmit a signal out ofthe computer apparatus 200 via a wired or wireless connection. Forexample, the output unit 240 can transmit a signal by converting thesignal in a format that is compatible with the destination hardware orsoftware, or by adjusting the signal to a response rate that iscompatible with the destination hardware or software.

The processor unit 220 comprises analysis means 221, first determinationmeans 222, and second determination means 223.

The analysis means 221 is configured to analyze a biological signalreceived by the receiver unit 210 and output feature data. Since thebiological signal is in a scalar quantity such as potential, absoluteamount of information is low. A large quantity of information can beidentified by analyzing a biological signal with the analysis means 221and producing feature data, which is multidimensional data. The analysismeans 221 can perform, for example, analysis and processing includingsmoothing, mathematical analysis such as frequency analysis, andparameter determination, on biological signals.

The first determination means 222 is configured to determine degrees ofsimilarly between feature data outputted by the analysis means 221 andeach of a plurality of teaching data. A plurality of teaching data isstored in the database unit 250. For example, the first determinationmeans 222 determines degrees of similarity between feature data and eachof a plurality of teaching data from an output of a neural network. Aneural network can be, for example, a feedforward neural network shownin FIG. 3.

FIG. 3 shows an example of a configuration of a neural network 300 usedby the first determination means 222. The neural network 300 has aninput layer, a hidden layer, and an output layer. The example in FIG. 3shows the neural network 300 as a tri-layer feedforward neural networkwith one layer of hidden layer, but the number of hidden layers is notlimited thereto. The neural network 300 can comprise one or more hiddenlayers. The number of nodes of the input layer of the neural network 300corresponds to the dimensionality of feature data. The number of nodesof the output layer of the neural network 300 corresponds to thedimensionality of teaching data, i.e., corresponds to the number ofpieces of information to be identified. The hidden layer of the neuralnetwork 300 can comprise any number of nodes. A weighting coefficient ofeach node of the hidden layer of the neural network 300 can becalculated based on a combination of feature data and teaching datastored in the database unit 250. For example, a weighting coefficient ofeach node can be calculated so that a value of the output layer whenfeature data is inputted into the input layer is a value of the teachingdata associated with the feature data. This can be performed for exampleby backpropagation (backward propagation of errors).

Each node of the output layer of the neural network 300 where theweighting coefficient of each node is calculated in this manner isassociated with information corresponding to each teaching data. Forexample, if the weighting coefficient for each node is calculated usinga combination of feature data obtained from a biological signalgenerated from a first motion and teaching data 1 (1.0, 0.0, 0.0, 0.0,0.0) corresponding to teaching signal “1” teaching that the motion is afirst motion, a combination of feature data obtained from a biologicalsignal generated from a second motion and teaching data 2 (0.0, 1.0,0.0, 0.0, 0.0) corresponding to teaching signal “2” teaching that themotion is a second motion, a combination of feature data obtained from abiological signal generated from a third motion and teaching data 3(0.0, 0.0, 1.0, 0.0, 0.0) corresponding to teaching signal “3” teachingthat the motion is a third motion, a combination of feature dataobtained from a biological signal generated from a fourth motion andteaching data 4 (0.0, 0.0, 0.0, 1.0, 0.0) corresponding to teachingsignal “4” teaching that the motion is a fourth motion, and acombination of feature data obtained from a biological signal generatedfrom a fifth motion and teaching data 5 (0.0, 0.0, 0.0, 0.0, 1.0)corresponding to teaching signal “5” teaching that the motion is a fifthmotion when information to be identified is a motion of an organism, thefirst node of the output layer of the neural network 300 is associatedwith the first motion, the second node is associated with the secondmotion, the third node is associated with the third motion, the fourthnode is associated with the fourth motion, and the fifth node isassociated with the fifth motion. Examples of ideal outputs of theneural network 300 with weighting coefficients for each node calculatedin this manner include an output of 1 by the first node of the outputlayer and an output of 0 by the other nodes when feature data obtainedfrom a biological signal upon performing the first motion is inputted.However, an ideal output is actually hardly ever obtained due to theeffect of noise or the like that coexists with a biological signal. Inactuality, one or more nodes of the output layer would output a value inthe range of 0 to 1. The value of each node of the output layercorresponds to the degree of similarity between the inputted featuredata and each teaching data corresponding to a motion to which each nodeis associated. If, for example, the output is (0.0, 0.2, 0.0, 0.8, 0.0),this indicates that inputted feature data is slightly similar toteaching data corresponding to the second motion associated with thesecond node, and is more similar to teaching data corresponding to thefourth motion associated with the fourth node, but are not similar toteaching data corresponding to motions associated with other nodes. If,for example, the output is (0.0, 0.0, 0.6, 0.0, 0.6), this indicatesthat inputted feature data is similar to both teaching datacorresponding to the third motion associated with the third node andteaching data corresponding to the fifth motion associated with thefifth node, but are not similar to teaching data corresponding tomotions associated with other nodes.

Referring again to FIG. 2, the second determination means 223 isconfigured to determine information represented by a biological signalbased on a plurality of degrees of similarity within a predeterminedperiod in degrees of similarity stored in a buffer of the memory unit230. For example, the second determination means 223 determines a degreeof similarity with high intensity of appearance as the “likely output”from a plurality of degrees of similarity within a predetermined periodin the degrees of similarity chronologically stored in the buffer of thememory unit 230, and determines information indicated by teaching datacorresponding to said degree of similarity as information represented bya biological signal from which feature data originates.

The memory unit 230 comprises a buffer for temporarily storinginformation. A buffer can, for example, temporarily store degrees ofsimilarity determined by the first determination means 222 for each timein chronological order. A buffer can, for example, delete old data oncea certain amount of data is stored, or delete data after a certainperiod of time has passed after storage.

FIG. 4 shows an example of a data configuration of an output vectorindicating the degrees of similarity with each teaching data stored in abuffer of the memory unit 230. The value of each component of an outputvector indicates the degree of similarity with corresponding teachingdata.

The buffer of the memory 230 chronologically stores output vectors foreach time. For example, if the degrees of similarity with each teachingdata 0 to 9 at time 1 are determined to be (0.0, 0.0, 0.2, 0.0, 0.5,0.7, 0.0, 0.0, 0.0, 0.0) by the first determination means 222, theresults are stored as an output vector indicating the degree ofsimilarity with each teaching data at time 1, and if the degrees ofsimilarity with each teaching data 0 to 9 at time 2 are determined to be(0.0, 0.0, 0.2, 0.0, 0.0, 0.7, 0.9, 0.0, 0.0, 0.0), the results arestored as an output vector indicating the degree of similarity with eachteaching data at time 2, and so on. The time and output vector arestored each time a degree of similarity is determined by the firstdetermination means 222 (see for example FIG. 4).

The example presented in FIG. 1 shows the biological signal detectionmeans 100 and the computer apparatus 200 to be separate constituentelements, but the present invention is not limited thereto. Thebiological signal detection means 100 and the computer apparatus 200 canalso be configured as a single constituent element.

In the example presented in FIG. 1, the database unit 250 is providedexternal to the computer apparatus 200, but the present invention is notlimited thereto. The database unit 250 can also be provided inside thecomputer apparatus 200. At this time, the database unit 250 can beimplemented by the same storage means as the storage means implementingthe memory unit 230, or implemented by storage means that is differentfrom the storage means implementing the memory unit 230. In either case,the database unit 250 is configured as a storage unit for the computerapparatus 200. The configuration of the database unit 250 is not limitedto a specific hardware configuration. For example, the database unit 250can be comprised of a single hardware component or a plurality ofhardware components. For example, the database unit 250 can beconfigured as an external hard disk apparatus of the computer apparatus200, or as cloud storage connected via a network.

In the example presented in FIG. 2, each constituent element of thecomputer apparatus 200 is provided within the computer apparatus 200,but the present invention is not limited thereto. Any of the constituentelements of the computer apparatus 200 can be provided external to thecomputer apparatus 200. For example, if each of the processor unit 220and the memory unit 230 is configured as separate hardware components,each hardware component can be connected via any network. In thisregard, any type of network can be used. For example, each hardwarecomponent can be connected via LAN, connected wirelessly, or connectedwith a wired connection.

While the aforementioned examples describe that the biological signaldetection means 100 extracts a scalar quantity from a detectedbiological signal and outputs the scalar quantity if the detectedbiological signal is coordinate dependent, the computer apparatus 200configured to extract a scalar quantity from a biological signalreceived from the biological signal detection means 100 is also withinthe scope of the present invention.

3. Processing for Identifying Information Represented by BiologicalSignal of the Invention

FIG. 5 shows an example of the processing for identifying informationrepresented by a biological signal of the invention. This processing isperformed in the system 10.

It is assumed that teaching data corresponding to a teaching signalinputted during the learning stage is stored while being associated withinputted feature data in the database unit 250, and the weightingcoefficient of each node of the hidden layer of the neural network 300shown in FIG. 3 is calculated based on a combination of feature data andteaching data stored in the database unit 250.

In step S501, the detection unit 110 of the biological signal detectionmeans 100 detects a biological signal. The detection unit 110 detects,for example, a myoelectric signal of an organism, a cardioelectricsignal of an organism, or a brainwave of an organism. If the detectionunit 110 detects a biological signal, the transmission unit 120 of thebiological signal detection means 100 transmits the detected biologicalsignal to the computer apparatus 200.

If the receiver unit 210 of the computer apparatus 200 receives abiological signal from the biological signal detection means 100, thereceiver unit 210 provides the received biological signal to theprocessor unit 220. In this regard, the receiver unit 210 can beconfigured to, for example, sequentially provide biological signals tothe processor unit 220 each time a biological signal is received, ortemporarily store received biological signals in the memory unit 230 andprovide the processor unit 220 with the signals at once after a certainamount of data is accumulated. Once the processor unit 220 is providedwith a biological signal, the procedure proceeds to step S502.

In step S502, the analysis means 221 of the processor unit 220 of thecomputer apparatus 200 analyzes the detected biological signal andoutputs feature data. The analysis means 221 outputs feature data byperforming analysis and processing including smoothing, mathematicalanalysis such as frequency analysis, or parameter determination. Theanalysis means 221 can be configured to process feature data at thistime by, for example, applying a weighting coefficient to a time seriesor frequency band of the outputted feature data.

In step S503, the first determination means 222 of the processor unit220 of the computer apparatus 200 determines degrees of similaritybetween feature data outputted in step S502 and each of the plurality ofteaching data. For example, the first determination means 222 inputs thefeature data outputted in step S502 into the neural network 300 shown inFIG. 3, and determines the degrees of similarity between feature dataand each of the plurality of teaching data from an output of the neuralnetwork 300.

The first determination means 222 can be configured, for example, toextract a part of feature data outputted in step S502 and input only theextracted feature data into the neural network 300 instead of inputtingall of the feature data outputted in step S502 into the neural network300. This can reduce the amount of computation in processing at a laterstage to prevent a decrease in the operational speed in the processingat the later stage. At this time, the first determination means 222 canextract feature data uniformly or non-uniformly. If feature data isextracted non-uniformly, the feature data is preferably extracted withan unbalanced weighting so that the portion of the feature data to befocused on is mainly extracted. This can prevent decreased precision inthe processing at a later stage due to extraction. Such extraction offeature data enables both high operational speed and precision in theprocessing at a later stage.

Once the first determination means 222 determines the degree ofsimilarity, the procedure proceeds to step S504.

In step S504, the buffer of the memory unit 230 of the computerapparatus 200 chronologically stores the degrees of similaritydetermined in step S503 for each time. For example, the buffer of thememory unit 230 chronologically stores an output vector indicatingdegrees of similarly as shown in FIG. 4 for each time. The processorunit 220 is preferably configured to repeat step S501 to step S504 untila predetermined amount of data is stored in the buffer. This enables theuse of a sufficient amount of data for degrees of similarity in theprocessing at a later stage and enhancement in precision of processingat a later stage. Once the buffer stores a degree of similarity, theprocessing proceeds to step S505.

In step S505, the second determination means 223 of the processor unit220 of the computer apparatus 200 determines information represented bya biological signal based on a plurality of degrees of similarity withina predetermined period in chronological degrees of similarity stored ina buffer. The predetermined period can be any period, with the last timeat which a degree of similarity is stored in a buffer being the endpoint. The predetermined period is, for example, any period of timewithin the range of about 10 ms to about 220 ms. The predeterminedperiod is for example about 80 ms to about 220 ms. This is because thesimple reaction time of humans is about 220 ms, so that it is difficultto change a motion being performed to another motion in less time thanthe simple reaction time. A predetermined period can be, for example,about 80 ms, about 200 ms, or the like. This is because a longerpredetermined period would improve stability of outputs, whereas a delaywould be greater so that responsiveness would be lower, and a delaygreater than 200 ms would lead to temporal inconsistency in self-bodyrecognition. The predetermined period can be appropriately determined bythose skilled in the art in accordance with, for example, theapplication, application program used, user needs, or the like whileconsidering the trade-off between the stability of outputs and humanreaction time. When used for example in a finger rehabilitationapplication described below, the predetermined period can be greaterthan 220 ms (e.g., about 220 to about 400 ms, such as about 300 ms,about 350 ms, or about 400 ms). This is because the time it takes tochange a motion being performed to another motion is longer forparalyzed patients as compared to healthy individuals.

For example, the second determination means 223 can be configured tocalculate a computed value for each of a plurality of teaching databased on a plurality of degrees of similarity within a predeterminedperiod and determine information represented by a biological signalbased on the computed value. For example, the second determination means223 can be configured to extract teaching data corresponding to thehighest computed value among the obtained computed values and determineinformation indicated by the extracted teaching data as informationrepresented by a biological signal. A computed value can be a valuecalculated by a known computation method or a computation method thatcan be conceived by those skilled in the art. A computed value can be,for example, a total value. As used herein, “total value” in thedetermination of information represented by a biological signal isunderstood by those skilled in the art as a concept that naturallyencompasses “mean value”. Specifically, the concept of calculating atotal value for each of the plurality of teaching data and extractingteaching data corresponding to the highest total value thereamongencompasses the concept of calculating a mean value for each of theplurality of teaching data and extracting teaching data corresponding tothe highest mean value thereamong. A computed value can be, for example,a probability or frequency of occurrence of a specific degree ofsimilarity. The probability of occurrence of a specific degree ofsimilarity is a value indicating the probability of a specific degree ofsimilarity occurring within a predetermined period. The frequency ofoccurrence of a specific degree of similarity can be a value indicatinghow much a specific degree of similarity occurred within a predeterminedperiod. In this regard, a specific degree of similarity can be, forexample, the highest degree of similarity in an output vector or adegree of similarity at or above a threshold value in an output vector.If, for example, 5 output vectors are outputted within a predeterminedperiod, and the highest degree of similarity is attained for a certainteaching data in 3 out of 5 output vectors, the frequency of occurrenceis 3 and the probability of occurrence is 3/5.

A certain number (e.g., one) of lowest numerical values can be excludedfrom calculation for each of a plurality of teaching data in thecalculation of a computed value (e.g., total value (mean value)). Thelowest numerical value in this regard can be the lowest numerical valueamong numerical values that are not 0.

Alternatively, the second determination means 223 can determineinformation indicated by teaching data corresponding to a computed valueexceeding a predetermined threshold value as information represented bya biological signal. If there is no computed value exceeding apredetermined threshold value at this time, the information would beunidentifiable. The information can also be unidentifiable if there area plurality of computed values exceeding a predetermined thresholdvalue. Alternatively, if there are a plurality of computed valuesexceeding a predetermined value, each information indicated by eachteaching data corresponding to a plurality of computed values exceedinga predetermined threshold value can be determined as informationrepresented by a biological signal. This is, for example, a case where aplurality of motions to be identified as concurrently performed, a casewherein a plurality of conditions to be identified are concurrentlyoccurring, or the like. If, for example, motions indicated by eachteaching data corresponding to two computed values exceeding apredetermined threshold value are “wrist bending motion” and “handgrasping motion”, it is presumed that a composite motion of bending awrist while grasping a hand is being performed, and “wrist bendingmotion” and “hand grasping motion” can be determined as motionsrepresented by a biological signal. If, for example, motions indicatedby each teaching data corresponding to two total values exceeding apredetermined threshold value are “hand grasping motion” and “wristrotating motion”, it is presumed that a composite motion of rotating awrist while grasping a hand (e.g., motion of holding and turning a doorknob) is being performed, and “hand grasping motion” and “wrist rotatingmotion” can be determined as motions represented by a biological signal.The second determination means 223 can identify any composite motionamong motions of the entire body in accordance with the detectedbiological signal. If at least two of a plurality of motions arecontradictory to one another (e.g., “hand opening motion” and “handgrasping motion”), the result can be deemed unidentifiable, or thesystem can be controlled as if muscle forces are exerted against eachother, with the joint being rigid.

Any value can be set as a predetermined threshold value. While a higherpredetermined threshold value leads to higher precision and stability,the percentage of cases with unidentifiable result would also be high,resulting in poor responsiveness. For example, a predetermined thresholdvalue can be fixed value or a variable. If a predetermined thresholdvalue is a variable, the predetermined threshold value can be, forexample, a given ratio (e.g., 90% to 60%, 80% to 60%, 70% to 60%, suchas 65%) with respect to the maximum value of the total value. Forexample, a predetermined value can be determined in accordance with thetotal number of data for a plurality of degrees of similarity within apredetermined period. If, for example, there is no total value exceedinga predetermined threshold value, the predetermined threshold value canbe reduced or increased until there is at least one total valueexceeding the threshold value. If, for example, there are a plurality oftotal values exceeding a threshold value, the predetermined thresholdvalue can be increased until there is one total value exceeding thepredetermined threshold value.

For example, a predetermined threshold value can be determined inaccordance with the biological signal to be detected. A predeterminedthreshold value can be determined, for example, in accordance with alocation where a biological signal is detected. For example, apredetermined threshold value used upon detection of a biological signalfrom an arm for identifying a hand motion and a predetermined thresholdvalue used upon detection of a biological signal from a leg foridentifying a walking motion can be the same or different values.

The precision of information represented by a biological signalidentified by the aforementioned processing improved significantly ascompared to the precision for identifying information represented by abiological signal directly from a degree of similarity outputted in stepS503. While the precision for identifying information represented by abiological signal directly from a degree of similarity outputted in stepS503 was about 80%, the precision of information represented by abiological signal identified by the aforementioned processing was veryhigh, reaching a percentage in the high 90s.

As an example, processing of the system 10 when a user performs a motioncorresponding to teaching data 5 among motions corresponding to teachingdata 0 to 9 is described with reference to FIGS. 6A to 6C. It is assumedthat a user is performing a motion corresponding to teaching data 5 fromtime 1 to time 5. In this regard, the processing at time 5 is described.It is assumed that the interval between each time is 20 ms, and thepredetermined period is 100 ms. It is assumed that output vectors fortime 1 to time 4 are already stored in the buffer of the memory unit 230as shown in FIG. 6A.

In step S501, the detection unit 110 of the biological signal detectionmeans 100 detects a biological signal originating from a motionperformed by a user.

In step S502, the analysis means 221 of the processor unit 220 of thecomputer apparatus 200 analyzes the detected biological signal andoutputs feature data.

In step S503, the first determination means 222 of the processor unit220 of the computer apparatus 200 determines degrees of similaritybetween the feature data outputted in step S502 and each of teachingdata 0 to 9. Inputting the feature data outputted in step S502 into theneural network 300 results in degrees of similarity (0.0, 0.9, 0.2, 0.0,0.0, 0.7, 0.0, 0.0, 0.0, 0.0) with each of teaching data 0 to 9 asoutputs.

In step S504, the buffer of the memory unit 230 of the computerapparatus 200 chronologically stores the degrees of similaritydetermined in step S503. The buffer of the memory unit 230 wouldchronologically store output vectors for time 1 to time 5 for each time,as shown in FIG. 6B.

In step S505, the second determination means 223 of the processor unit220 of the computer apparatus 200 determines a motion represented by abiological signal originating from a motion performed by a user based ona plurality of degrees of similarity within a predetermined period inchronological degrees of similarity stored in the buffer. First, thesecond determination means 223 obtains a total value by totaling degreesof similarity for time 1 to time 5 corresponding to the predeterminedperiod for each teaching data, as shown in FIG. 6B. Next, the seconddetermination means 223 determines the motion indicated by teaching datafor “5” corresponding to the highest total value as the motionrepresented by a biological signal detected at time 5. Alternatively, ifthe predetermined threshold value is 2.5, the second determination means223 determines the motion indicated by teaching data for “5”corresponding to a total value exceeding a predetermined threshold valueas the motion represented by a biological signal detected at time 5. Inthis manner, the system 10 of the invention can appropriately identifythe motion performed by a user.

If, for example, a motion represented by a biological signal were to beidentified directly from the degrees of similarity obtained in stepS503, the motion would be incorrectly identified as the motioncorresponding to teaching data “1” because the teaching datacorresponding to the highest degree of similarity would be teaching data“1”. In contrast, the system 10 of the invention determines a motionrepresented by a biological signal based on a plurality of degrees ofsimilarity within a predetermined period in chronological degrees ofsimilarity stored in a buffer, so that a motion represented by abiological signal can be identified with very high precision.

The processing performed when a user performs a motion corresponding toteaching data 5 at time 6 is also the same.

In step S501, the detection unit 110 of the biological signal detectionmeans 100 detects a biological signal originating from a motionperformed by a user. In step S502, the analysis means 221 of theprocessor unit 220 of the computer apparatus 200 analyzes the detectedbiological signal and outputs feature data. In step S503, the firstdetermination means 222 of the processor unit 220 of the computerapparatus 200 determines degrees of similarity between the feature dataoutputted in step S502 and each of teaching data 0 to 9. Inputting thefeature data outputted in step S502 into the neural network 300 resultedin degrees of similarity (0.0, 0.0, 0.2, 0.0, 0.0, 0.7, 0.9, 0.0, 0.0,0.0) with each of teaching data 0 to 9 as outputs.

In step S504, the buffer of the memory unit 230 of the computerapparatus 200 chronologically stores the degrees of similaritydetermined in step S503. The buffer of the memory unit 230chronologically stores output vectors for time 1 to time 6 for each timeas shown in FIG. 6C.

In step S505, the second determination means 223 of the processor unit220 of the computer apparatus 200 determines a motion represented by abiological signal originating from a motion performed by a user based ona plurality of degrees of similarity within a predetermined period inchronological degrees of similarity stored in the buffer. First, thesecond determination means 223 obtains a total value by totaling degreesof similarity for time 2 to time 6 corresponding to the predeterminedperiod for each teaching data, as shown in FIG. 6C. Next, the seconddetermination means 223 extracts teaching data for “5” corresponding tothe highest total value and determines the motion indicated by theextracted teaching data for “5” as the motion represented by abiological signal detected at time 6. Alternatively, if thepredetermined threshold value is 2.5, the second determination means 223determines the motion indicated by teaching data for “5” correspondingto a total value exceeding a predetermined threshold value as a motionrepresented by a biological signal detected at time 6. In this manner,the system 10 of the invention can appropriately identify the motionperformed by a user.

If, for example, a motion represented by a biological signal were to beidentified directly from the degrees of similarity obtained in stepS503, the motion would be incorrectly identified as the motioncorresponding to teaching data 6 because the teaching data correspondingto the highest degree of similarity would be teaching data 6. Incontrast, the system 10 of the invention determines a motion representedby a biological signal based on a plurality of degrees of similaritywithin a predetermined period in chronological degrees of similaritystored in a buffer, so that a motion represented by a biological signalcan be similarly identified with very high precision at time 6.

As another example, processing of the system 10 when a user performs acomposite motion of a motion corresponding to teaching data 3 and amotion corresponding to teaching data 7 among motions corresponding toteaching data 0 to 9 is described with reference to FIGS. 6D to 6F. Itis assumed that a user is performing a motion corresponding to teachingdata 3 and a motion corresponding to teaching data 7 from time 1 to time5. In this regard, the processing at time 5 is described. It is assumedthat the interval between each time is 20 ms, and the predeterminedperiod is 100 ms. It is assumed that output vectors for time 1 to time 4are already stored in the buffer of the memory unit 230 as shown in FIG.6D.

In step S501, the detection unit 110 of the biological signal detectionmeans 100 detects a biological signal originating from a motionperformed by a user.

In step S502, the analysis means 221 of the processor unit 220 of thecomputer apparatus 200 analyzes the detected biological signal andoutputs feature data.

In step S503, the first determination means 222 of the processor unit220 of the computer apparatus 200 determines degrees of similaritybetween the feature data outputted in step S502 and each of teachingdata 0 to 9. Inputting the feature data outputted in step S502 into theneural network 300 results in degrees of similarity (0.0, 0.0, 0.2, 0.9,0.2, 0.1, 0.0, 0.8, 0.0, 0.9) with each of teaching data 0 to 9 asoutputs.

In step S504, the buffer of the memory unit 230 of the computerapparatus 200 chronologically stores the degrees of similaritydetermined in step S503. The buffer of the memory unit 230 wouldchronologically store output vectors for time 1 to time 5 for each time,as shown in FIG. 6E.

In step S505, the second determination means 223 of the processor unit220 of the computer apparatus 200 determines a motion represented by abiological signal originating from a motion performed by a user based ona plurality of degrees of similarity within a predetermined period inchronological degrees of similarity stored in the buffer. First, thesecond determination means 223 obtains a total value by totaling degreesof similarity for time 1 to time 5 corresponding to the predeterminedperiod for each teaching data, as shown in FIG. 6E. Next, the seconddetermination means 223 determines the motion indicated by teaching dataexceeding a predetermined threshold value as the motion represented by abiological signal detected at time 5. If, for example, the predeterminedthreshold value is a fixed value 3.5, the second determination means 223determines the motion indicated by teaching data “3” and the motionindicated by teaching data “7” corresponding to a total value exceedinga predetermined threshold value as a motion represented by a biologicalsignal detected at time 5. In this manner, the system 10 of theinvention can appropriately and simultaneously identify the compositemotion performed by a user.

If, for example, a motion represented by a biological signal were to beidentified directly from the degrees of similarity obtained in stepS503, the motion would be incorrectly identified as the motioncorresponding to teaching data “9” because the teaching datacorresponding to the highest degree of similarity would be teaching data“9”. In contrast, the system 10 of the invention determines a motionrepresented by a biological signal based on a plurality of degrees ofsimilarity within a predetermined period in chronological degrees ofsimilarity stored in a buffer, so that a motion represented by abiological signal can be identified with very high precision, even for acomposite motion.

The processing performed when a user performs a motion corresponding toteaching data 5 at time 6 is also the same.

In step S501, the detection unit 110 of the biological signal detectionmeans 100 detects a biological signal originating from a motionperformed by a user. In step S502, the analysis means 221 of theprocessor unit 220 of the computer apparatus 200 analyzes the detectedbiological signal and outputs feature data. In step S503, the firstdetermination means 222 of the processor unit 220 of the computerapparatus 200 determines degrees of similarity between the feature dataoutputted in step S502 and each of teaching data 0 to 9. Inputting thefeature data outputted in step S502 into the neural network 300 resultedin degrees of similarity (0.2, 0.9, 0.2, 0.7, 0.3, 0.1, 0.0, 0.7, 0.0,0.1) with each of teaching data 0 to 9 as outputs.

In step S504, the buffer of the memory unit 230 of the computerapparatus 200 chronologically stores the degrees of similaritydetermined in step S503. The buffer of the memory unit 230chronologically stores output vectors for time 1 to time 6 for each timeas shown in FIG. 6F.

In step S505, the second determination means 223 of the processor unit220 of the computer apparatus 200 determines a motion represented by abiological signal originating from a motion performed by a user based ona plurality of degrees of similarity within a predetermined period inchronological degrees of similarity stored in the buffer. First, thesecond determination means 223 obtains a total value by totaling degreesof similarity for time 2 to time 6 corresponding to the predeterminedperiod for each teaching data, as shown in FIG. 6F. Next, the seconddetermination means 223 determines the motion indicated by teaching dataexceeding a predetermined threshold value as the motion represented by abiological signal detected at time 6. If, for example, the predeterminedthreshold value is a fixed value 3.5, the second determination means 223determines the motion indicated by teaching data “3” and the motionindicated by teaching data “7” corresponding to a total value exceedinga predetermined threshold value as a motion represented by a biologicalsignal detected at time 6. In this manner, the system 10 of theinvention can appropriately and simultaneously identify the compositemotion performed by a user.

If, for example, a motion represented by a biological signal were to beidentified directly from the degrees of similarity obtained in stepS503, the motion would be incorrectly identified as the motioncorresponding to teaching data “1” because the teaching datacorresponding to the highest degree of similarity would be teaching data“1”. In contrast, the system 10 of the invention determines a motionrepresented by a biological signal based on a plurality of degrees ofsimilarity within a predetermined period in chronological degrees ofsimilarity stored in a buffer, so that a motion represented by abiological signal can be similarly identified with very high precisionat time 6, even for a composite motion.

4. Application Example

The system 10 of the invention can be used in any application wherein itis useful to detect a biological signal resulting in some type of anoutput, but the system is preferably for, but not limited to, fingerrehabilitation, swallow diagnosis, wheelchair, prosthetic hand,prosthetic arm, prosthetic foot, robot, upper limb assisting apparatus,lower limb assisting apparatus, or trunk assisting apparatus. If, forexample, the system is for a robot, the system can be applied to, forexample, the entire robot or a part of a robot such as a robot arm or arobot hand.

The system 10 of the invention can comprise, in accordance with theapplication, wearing means for wearing the system on a part of the bodyof a subject whose biological signal is to be analyzed. Such a part ofthe body can be, but is not limited to, the upper limb, abdomen, neck,lower limb, or back of the subject emitting a biological signal. A partof the body can be any part of the body. Wearing means can be anywearing means, such as a belt or sticker for wearing the system on apart of the body of a subject. If, for example, the system 10 of theinvention is for a robot arm, the system can be configured to detect amyoelectric signal of a muscle of an upper limb and move the robot armbased on information represented by the detected myoelectric signal. Insuch a case, a user can, for example, make a robot arm mimic a motionintended by the user by using the system 10 of the invention. Since thesystem 10 of the invention can identify not only a simple motion butalso a composite motion, the system can make a robot arm mimic even acomposite motion with high precision. Alternatively, if the system 10 ofthe invention is for robot arms, the system can be configured to detecta myoelectric signal of a muscle of a body part other than the forearm(e.g., facial muscle) and move the robot arm based on informationrepresented by the detected myoelectric signal. In such a case, a usercan, for example, operate a robot arm by the user's own predeterminedmotion as a command by using the system 10 of the invention. Thisenables operation of a robot arm even by, for example, patients withupper limb paralysis. Since the system 10 of the invention can identifynot only a simple motion but also a composite motion, the number ofcommands for operation can be increased compared to operations using asimple motion as a command. Specifically, use of a composite motion as acommand enables a robot arm to perform more motions with fewer types ofuser motions.

In a preferred embodiment, the system 10 of the invention can be appliedto, for example, a finger rehabilitation apparatus.

For a patient with paralysis in a finger in need of rehabilitationthereof (e.g., stroke patient), the biological signal level would belower. Thus, the precision of identifying a motion represented by abiological signal is lower as compared to healthy individuals. However,a motion represented by a biological signal can be identified with veryhigh precision with the system 10 of the invention, so that theidentification precision can be enhanced to a level that is usable inrehabilitation even if the biological signal level is low. The system 10of the invention can precisely and simultaneously identify not only asimple motion of a finger, but also a composite motion of a finger. Forthis reason, the efficiency and effect of rehabilitation of a finger isdramatically improved by accurately and rapidly identifying a motionintended by a patient and correctly and immediately assisting theintended motion. For example, it is known that plasticity of the brainis promoted and recovery of the paralyzed function is promoted byactually performing and repeating a motion intended by a patient.

A finger rehabilitation apparatus comprising the system 10 of theinvention comprises wearing means that enables the biological signaldetection means 100 to be worn on the skin of an upper limb of a user.For example, wearing means can be any means such as a belt or stickerfor wearing the detection unit 111 of the biological signal detectionmeans 100 on the skin of an upper limb (e.g., upper arm or forearm) of auser.

For example, an output from a finger rehabilitation apparatus can bedisplayed on display means such as a display and presented to arehabilitation trainer. A rehabilitation trainer can provide pertinentrehabilitation guidance based on an output from a finger rehabilitationapparatus, leading to efficient and effective rehabilitation.

A finger rehabilitation apparatus comprising the system 10 of theinvention can comprise a finger movement assisting apparatus worn on afinger of a user. A finger movement assisting apparatus is configured toact on a finger of a user so as to assist the movement of the finger ofthe user. A finger movement assisting apparatus can be configured, forexample, to drive a finger joint with a pneumatic actuator or to drive afinger joint by the torque of a motor.

A finger movement assisting apparatus can be, for example, a fingermovement assisting apparatus 700 shown in FIGS. 7A and 7B.

FIG. 7A shows the outer appearance of the finger movement assistingapparatus 700. FIG. 7B shows the finger movement assisting apparatus 700worn on a finger of a user.

The finger movement assisting apparatus 700 comprises a main body 710, apalm bolt 720 extending from the main body 710, an arm 730, and a fingerbolt 740 extending from the arm 730. The arm 730 is configured to bepivotable with respect to the main body 710. The arm 730 can beconfigured to be pivoted by a motor, by a pneumatic actuator, or by awire.

In a finger rehabilitation apparatus comprising the system 10 of theinvention, an output from the system 10 of the invention can be providedto the finger movement assisting apparatus. A finger movement assistingapparatus acts to assist the motion intended by a patient based on anoutput from a finger rehabilitation apparatus. For example, an outputfrom the system 10 of the invention can be provided to the fingermovement assisting apparatus 700. If the finger movement assistingapparatus 700 is worn on a finger as shown in FIG. 7B, the finger bolt740 assists the motion of the finger by a pivotal motion of the arm 730.For example, the finger bolt 740 pushes the finger up by a pivotalmotion of the arm 730, which can assist the hand opening motion of auser. In this manner, patients can rehabilitate by their own will,leading to efficient and effective rehabilitation.

In another preferred embodiment, the system 10 of the invention can beapplied to, for example, a swallow diagnosis apparatus.

If a myoelectric signal is to be obtained around the neck, it isdifficult to identify the motion from which the myoelectric signal hasoriginated because a plurality of myoelectric signals indicatingactivity of a plurality of muscles coexist. Thus, the identificationprecision decreases. However, since a motion represented by a biologicalsignal can be identified at very high precision with the system 10 ofthe invention, identification precision can be improved to a level whereswallowing impairment can be diagnosed, even if myoelectric signalscoexist. The system 10 of the invention can not only precisely identifysimple motions in jaw and oral movements, but also composite motions injaw and oral movements. This enables correct swallow diagnosis based ona myoelectric signal, for example, by identifying whether the motion isswallowing motion when suffering from a swallowing impairment or ahealthy swallowing motion without a swallowing impairment.

A swallow diagnosis apparatus comprising the system 10 of the inventioncomprises wearing means that enables the biological signal detectionmeans 100 to be worn on the skin of a neck of a user. Wearing means canbe any means such as a belt or a sticker for wearing the detection unit111 of the biological signal detection means 100 on the skin of a neckof a user. A swallow diagnosis apparatus comprising the system 10 of theinvention can comprise means that enables the biological signaldetection means 100 to be in contact with, without fixing the means to,the skin of a neck of a user in addition or in place of the wearingmeans. Such means can detect a biological signal of a patient, forexample, by pressing the means onto a patient in the same manner as astethoscope.

For example, an output from a swallow diagnosis apparatus can bedisplayed on display means such as a display and presented to aphysician. A physician can render an accurate diagnosis based on anoutput from a swallow diagnosis apparatus. Alternatively, an output froma swallow diagnosis apparatus can be displayed on a display or the likeand presented to the users themselves. This enables users to accuratelyself-diagnose a swallowing impairment based on an output from a swallowdiagnosis apparatus.

EXAMPLES Example 1. Identification of Myoelectric Signal on the Skin ofan Upper Limb

A subject (healthy male in his 20s) was asked to wear a myoelectricsensor on the upper limb and move firmly with force to study thecorresponding relationship between identification of a myoelectricsignal and motion. The myoelectric sensor comprised an amp unit, a 500Hz low pass filter, a 10 Hz high pass filter, and a 50 Hz notch filter.The experiment was conducted with 80 ms as the predetermined period.

The test results are shown in FIGS. 8 and 9. FIGS. 8(a) and 9(a) aregraphs of results of processing using the system 10 of the invention.FIGS. 8(b) and 9(b) are graphs of results of identifying directly fromdegrees of similarity obtained in step S503, i.e., from the output ofthe neural network 300. FIGS. 9(a) to 9(b) are diagrams expanding thedotted line portions shown in FIGS. 8(a) to 8(b).

The vertical axis of the graphs indicates motion ID. 0 is “no motion”, 1is “wrist supine motion”, 3 is “wrist bending motion”, 4 is “wriststretching motion”, 5 is “fist clenching motion”, 7 is “thumb bendingmotion”, and 9 is “ring finger, pinky finger bending motion”. It isassumed for the neural network 300 that a weighting coefficient of eachnode is calculated so that each node of an output layer is associatedwith a motion corresponding to a motion ID. The horizontal axis of thegraphs is the number of execution steps. 50 steps were performed persecond. Specifically, each step interval is 20 ms.

A subject performed motions in the order of “wrist supine motion”(motion ID: 1), “ring finger, pinky finger bending motion” (motion ID:9), “thumb bending motion” (motion ID: 7), “wrist stretching motion”(motion ID: 4), “wrist bending motion” (motion ID: 3), and “fistclenching motion” (motion ID: 5).

The dotted lines in each graph indicate the ideal state of 100%identification rate.

As can be understood from FIG. 8, FIG. 8(a) is a graph that mostlyfollows the dotted lines. It can be understood that processing with thesystem 10 of the invention attained excellent identification rate.

As can be understood from FIG. 9, FIG. 9(a) is a graph that more closelyfollows the dotted lines than FIG. 9(b) even for the “wrist stretchingmotion” (motion ID: 4) which tends to be difficult to identify. It canbe understood that the system 10 of the invention accurately identifiesthe motions represented by a myoelectric signal.

The identification rate is calculated from each graph. Theidentification rate of FIGS. 8(a) and 9(a) was 98.8%, and theidentification rate of FIGS. 8(b) and 9(b) was 79.9%.

In this manner, the system 10 of the invention was demonstrated to becapable of identifying a motion represented by a biological signal atvery high precision.

Example 2. Identification of Myoelectric Signal on the Skin of an UpperLimb of Subject with Low Myoelectric Signal Level

A subject (healthy male in his 20s) was asked to wear a myoelectricsensor on the upper limb. The corresponding relationship betweenidentification of a myoelectric signal and motion was studied in thesame manner as Example 1, except for asking the subject to move withminimal force.

The test results are shown in FIGS. 10 and 11. FIGS. 10(a) and 11(a) aregraphs of results from processing with the system 10 of the invention.FIGS. 10(b) and 11(b) are graphs of results from processing using thealgorithm described below. FIGS. 10(c) and 11(c) are graphs of resultsof identifying directly from the degrees of similarity obtained in stepS503, i.e., outputs of the neural network 300. FIGS. 11(a) to 11(c) arediagrams expanding the dotted line portions shown in FIGS. 10(a) to10(c).

The algorithm used in FIGS. 10(b) and 11(b) is an algorithm forchronologically storing results of identifying directly from the degreesof similarity obtained in step S503, i.e., outputs of the neural network300, for each time in a buffer, and determining an identification resultwith a share within the buffer equal to or greater than a thresholdvalue as a motion represented by a myoelectric signal.

The vertical axis of the graphs indicates motion ID. 0 is “no motion”, 1is “wrist supine motion”, 3 is “wrist bending motion”, 4 is “wriststretching motion”, 5 is “fist clenching motion”, 7 is “thumb bendingmotion”, and 9 is “ring finger, pinky finger bending motion”. It isassumed for the neural network 300 that a weighting coefficient of eachnode is calculated so that each node of an output layer is associatedwith a motion corresponding to a motion ID. The horizontal axis of thegraphs is the number of execution steps. 50 steps were performed persecond. Specifically, each step interval is 20 ms.

A subject performed motions in the order of “fist clenching motion”(motion ID: 5), “wrist bending motion” (motion ID: 3), “wrist stretchingmotion” (motion ID: 4), “thumb bending motion” (motion ID: 7), “ringfinger, pinky finger bending motion” (motion ID: 9), and “wrist supinemotion” (motion ID: 1).

The dotted lines in each graph indicate the ideal state of 100%identification rate.

As can be understood especially from FIG. 11, FIG. 11(a) is a graph thatmore closely follows the dotted lines than FIG. 11(b) even for the“thumb bending motion” (motion ID: 7), which tends to be difficult toidentify with low myoelectric signal levels. It can be understood thatthe system 10 of the invention accurately identifies the motionsrepresented by a myoelectric signal.

In this manner, the system 10 of the invention was demonstrated to becapable of identifying a motion represented by a biological signal atvery high precision, even with a low biological signal level.

The present invention is not limited to the aforementioned embodiments.It is understood that the scope of the present invention should beinterpreted solely from the scope of the claims. It is understood thatthose skilled in the art can implement an equivalent scope, based on thedescriptions of the invention and common general knowledge, from thedescriptions of the specific preferred embodiments of the invention.

INDUSTRIAL APPLICABILITY

The present invention is useful for providing a system for identifyinginformation represented by a biological signal, which enables enhancedprecision to identify a biological signal, and a finger rehabilitationapparatus and a swallow diagnosis apparatus comprising the same.

REFERENCE SIGNS LIST

-   10 System-   100 Biological signal detection means-   200 Computer apparatus-   250 Database unit

The invention claimed is:
 1. A system for identifying informationrepresented by a biological signal, the system comprising: detectionmeans for detecting a biological signal; analysis means for analyzingthe detected biological signal and outputting feature data; firstdetermination means for determining output vector indicating degrees ofsimilarity between the feature data and each of a plurality of teachingdata; storage means for chronologically storing the output vectors foreach time; and second determination means for determining informationrepresented by the biological signal based on a plurality of outputvectors within a predetermined period in the chronological outputvectors stored in the storage means, wherein the second determinationmeans: calculates computed values for each one of the plurality ofteaching data based on corresponding chronological components of theplurality of output vectors within the predetermined period, wherein anend point of the predetermined period is a last time at which an outputvector is stored in the storage means, wherein when a new subsequentoutput vector is stored, a start point of the predetermined period andthe end point move according to the new subsequent output vector, andthe output vectors which fall outside of the predetermined period arenot used to calculate the computed values; and determines theinformation represented by the biological signal based on the computedvalues.
 2. The system of claim 1, wherein the second determination meansextracts teaching data corresponding to the highest computed value amongthe computed values and determines information indicated by theextracted teaching data as the information represented by the biologicalsignal.
 3. The system of claim 1, wherein the second determination meansextracts at least one teaching data corresponding to a computed valueexceeding a predetermined threshold value among the computed values anddetermines information indicated by the extracted teaching data as theinformation represented by the biological signal.
 4. The system of claim3, wherein the second determination means extracts a plurality ofteaching data corresponding to computed values exceeding thepredetermined threshold value among the computed values and determinesinformation indicated by each of the plurality of extracted teachingdata as the information represented by the biological signal.
 5. Thesystem of claim 4, wherein the information represented by the biologicalsignal indicates that a composite motion has been performed.
 6. Thesystem of claim 1, wherein the computed values are total values.
 7. Thesystem of claim 1, wherein the storage means is a buffer for temporarilystoring information, and the output vectors are temporarily stored inthe buffer.
 8. The system of claim 1, wherein the predetermined periodis about 10 to 200 ms.
 9. The system of claim 1, further comprisingwearing means for wearing the detection means on a body of a subject.10. The system of claim 9, wherein the body is an upper limb, anabdomen, a neck, a lower limb, or a back of the subject.
 11. The systemof claim 1 for finger rehabilitation, for swallow diagnosis, for awheelchair, for a prosthetic hand, for a prosthetic arm, for aprosthetic foot, for a robot, for an upper limb assisting apparatus, fora lower limb assisting apparatus, or for a trunk assisting apparatus.12. A finger rehabilitation apparatus comprising: the system of claim 1;and a finger movement assisting apparatus.
 13. A swallow diagnosisapparatus comprising the system of claim 1.